{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data block API foundations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from exp.nb_07a import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://s3.amazonaws.com/fast-ai-imageclas/imagenette-160'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.URLs.IMAGENETTE_160"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image ItemList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously we were reading in to RAM the whole MNIST dataset at once, loading it as a pickle file. We can't do that for datasets larger than our RAM capacity, so instead we leave the images on disk and just grab the ones we need for each mini-batch as we use them.\n",
    "\n",
    "Let's use the [imagenette dataset](https://github.com/fastai/imagenette/blob/master/README.md) and build the data blocks we need along the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/ubuntu/.fastai/data/imagenette-160')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = datasets.untar_data(datasets.URLs.IMAGENETTE_160)\n",
    "path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to look at what's inside a directory from a notebook, we add the `.ls` method to `Path` with a monkey-patch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import PIL,os,mimetypes\n",
    "Path.ls = lambda x: list(x.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/models')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03445777'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03425413'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n01440764'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03028079'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n02979186'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03394916'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n02102040'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03417042'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03000684')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(path/'val').ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look inside a class folder (the first class is tench):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_tench = path/'val'/'n01440764'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n01440764/ILSVRC2012_val_00017995.JPEG')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_fn = path_tench.ls()[0]\n",
    "img_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=213x160 at 0x7FB114DBF2E8>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = PIL.Image.open(img_fn)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb0cf4b5320>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "imga = numpy.array(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 213, 3)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imga.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, ..., 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, ..., 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, ..., 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, ..., 1, 1, 1, 1],\n",
       "       ...,\n",
       "       [1, 1, 1, 1, ..., 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, ..., 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, ..., 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, ..., 1, 1, 1, 1]], dtype=uint8)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imga[:10,:10,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just in case there are other files in the directory (models, texts...) we want to keep only the images. Let's not write it out by hand, but instead use what's already on our computer (the MIME types database)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "image_extensions = set(k for k,v in mimetypes.types_map.items() if v.startswith('image/'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.svg .tif .ras .png .ppm .tiff .gif .jpe .xbm .pnm .jpg .jpeg .xpm .bmp .xwd .ief .rgb .ico .pgm .pbm'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "' '.join(image_extensions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def setify(o): return o if isinstance(o,set) else set(listify(o))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_eq(setify('aa'), {'aa'})\n",
    "test_eq(setify(['aa',1]), {'aa',1})\n",
    "test_eq(setify(None), set())\n",
    "test_eq(setify(1), {1})\n",
    "test_eq(setify({1}), {1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's walk through the directories and grab all the images. The first private function grabs all the images inside a given directory and the second one walks (potentially recursively) through all the folder in `path`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=1325)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def _get_files(p, fs, extensions=None):\n",
    "    p = Path(p)\n",
    "    res = [p/f for f in fs if not f.startswith('.')\n",
    "           and ((not extensions) or f'.{f.split(\".\")[-1].lower()}' in extensions)]\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/ILSVRC2012_val_00017995.JPEG'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/ILSVRC2012_val_00009379.JPEG'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/ILSVRC2012_val_00003014.JPEG')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = [o.name for o in os.scandir(path_tench)]\n",
    "t = _get_files(path, t, extensions=image_extensions)\n",
    "t[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def get_files(path, extensions=None, recurse=False, include=None):\n",
    "    path = Path(path)\n",
    "    extensions = setify(extensions)\n",
    "    extensions = {e.lower() for e in extensions}\n",
    "    if recurse:\n",
    "        res = []\n",
    "        for i,(p,d,f) in enumerate(os.walk(path)): # returns (dirpath, dirnames, filenames)\n",
    "            if include is not None and i==0: d[:] = [o for o in d if o in include]\n",
    "            else:                            d[:] = [o for o in d if not o.startswith('.')]\n",
    "            res += _get_files(p, f, extensions)\n",
    "        return res\n",
    "    else:\n",
    "        f = [o.name for o in os.scandir(path) if o.is_file()]\n",
    "        return _get_files(path, f, extensions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n01440764/ILSVRC2012_val_00017995.JPEG'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n01440764/ILSVRC2012_val_00009379.JPEG'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n01440764/ILSVRC2012_val_00003014.JPEG')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_files(path_tench, image_extensions)[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need the recurse argument when we start from `path` since the pictures are two level below in directories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00016387.JPEG'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00034544.JPEG'),\n",
       " PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00009593.JPEG')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_files(path, image_extensions, recurse=True)[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13394"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_fns = get_files(path, image_extensions, recurse=True)\n",
    "len(all_fns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagenet is 100 times bigger than imagenette, so we need this to be fast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72.6 ms ± 134 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 10 get_files(path, image_extensions, recurse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare for modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we need to do:\n",
    "\n",
    "- Get files\n",
    "- Split validation set\n",
    "  - random%, folder name, csv, ...\n",
    "- Label: \n",
    "  - folder name, file name/re, csv, ...\n",
    "- Transform per image (optional)\n",
    "- Transform to tensor\n",
    "- DataLoader\n",
    "- Transform per batch (optional)\n",
    "- DataBunch\n",
    "- Add test set (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=1728)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `ListContainer` class from notebook 06 to store our objects in an `ItemList`. The `get` method will need to be subclassed to explain how to access an element (open an image for instance), then the private `_get` method can allow us to apply any additional transform to it.\n",
    "\n",
    "`new` will be used in conjunction with `__getitem__` (that works for one index or a list of indices) to create training and validation set from a single stream when we split the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def compose(x, funcs, *args, order_key='_order', **kwargs):\n",
    "    key = lambda o: getattr(o, order_key, 0)\n",
    "    for f in sorted(listify(funcs), key=key): x = f(x, **kwargs)\n",
    "    return x\n",
    "\n",
    "class ItemList(ListContainer):\n",
    "    def __init__(self, items, path='.', tfms=None):\n",
    "        super().__init__(items)\n",
    "        self.path,self.tfms = Path(path),tfms\n",
    "\n",
    "    def __repr__(self): return f'{super().__repr__()}\\nPath: {self.path}'\n",
    "    \n",
    "    def new(self, items, cls=None):\n",
    "        if cls is None: cls=self.__class__\n",
    "        return cls(items, self.path, tfms=self.tfms)\n",
    "    \n",
    "    def  get(self, i): return i\n",
    "    def _get(self, i): return compose(self.get(i), self.tfms)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        res = super().__getitem__(idx)\n",
    "        if isinstance(res,list): return [self._get(o) for o in res]\n",
    "        return self._get(res)\n",
    "\n",
    "class ImageList(ItemList):\n",
    "    @classmethod\n",
    "    def from_files(cls, path, extensions=None, recurse=True, include=None, **kwargs):\n",
    "        if extensions is None: extensions = image_extensions\n",
    "        return cls(get_files(path, extensions, recurse=recurse, include=include), path, **kwargs)\n",
    "    \n",
    "    def get(self, fn): return PIL.Image.open(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforms aren't only used for data augmentation. To allow total flexibility, `ImageList` returns the raw PIL image. The first thing is to convert it to 'RGB' (or something else).\n",
    "\n",
    "Transforms only need to be functions that take an element of the `ItemList` and transform it. If they need state, they can be defined as a class. Also, having them as a class allows to define an `_order` attribute (default 0) that is used to sort the transforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Transform(): _order=0\n",
    "\n",
    "class MakeRGB(Transform):\n",
    "    def __call__(self, item): return item.convert('RGB')\n",
    "\n",
    "def make_rgb(item): return item.convert('RGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "il = ImageList.from_files(path, tfms=make_rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ImageList (13394 items)\n",
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00016387.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00034544.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00009593.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00029149.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00037770.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00009370.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00031268.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00047147.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00031035.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00020698.JPEG')...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "il"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x200 at 0x7FB0CF42D358>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = il[0]; img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also index with a range or a list of integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<PIL.Image.Image image mode=RGB size=160x200 at 0x7FB0CF42D048>]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "il[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we need to split the files between those in the folder train and those in the folder val."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00016387.JPEG')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = il.items[0]; fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our filenames are `path` object, we can find the directory of the file with `.parent`. We need to go back two folders before since the last folders are the class names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'val'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.parent.parent.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=2175)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def grandparent_splitter(fn, valid_name='valid', train_name='train'):\n",
    "    gp = fn.parent.parent.name\n",
    "    return True if gp==valid_name else False if gp==train_name else None\n",
    "\n",
    "def split_by_func(items, f):\n",
    "    mask = [f(o) for o in items]\n",
    "    # `None` values will be filtered out\n",
    "    f = [o for o,m in zip(items,mask) if m==False]\n",
    "    t = [o for o,m in zip(items,mask) if m==True ]\n",
    "    return f,t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "splitter = partial(grandparent_splitter, valid_name='val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 38.4 ms, sys: 169 µs, total: 38.6 ms\n",
      "Wall time: 38.2 ms\n"
     ]
    }
   ],
   "source": [
    "%time train,valid = split_by_func(il, splitter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12894, 500)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train),len(valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we can split our data, let's create the class that will contain it. It just needs two `ItemList` to be initialized, and we create a shortcut to all the unknown attributes by trying to grab them in the `train` `ItemList`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class SplitData():\n",
    "    def __init__(self, train, valid): self.train,self.valid = train,valid\n",
    "        \n",
    "    def __getattr__(self,k): return getattr(self.train,k)\n",
    "    #This is needed if we want to pickle SplitData and be able to load it back without recursion errors\n",
    "    def __setstate__(self,data:Any): self.__dict__.update(data) \n",
    "    \n",
    "    @classmethod\n",
    "    def split_by_func(cls, il, f):\n",
    "        lists = map(il.new, split_by_func(il.items, f))\n",
    "        return cls(*lists)\n",
    "\n",
    "    def __repr__(self): return f'{self.__class__.__name__}\\nTrain: {self.train}\\nValid: {self.valid}\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SplitData\n",
       "Train: ImageList (12894 items)\n",
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_9403.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_6402.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_4446.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_22655.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_29390.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_17004.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_8837.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_19451.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_12883.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_13476.JPEG')...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160\n",
       "Valid: ImageList (500 items)\n",
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00016387.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00034544.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00009593.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00029149.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00037770.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00009370.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00031268.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00047147.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00031035.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00020698.JPEG')...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd = SplitData.split_by_func(il, splitter); sd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Labeling has to be done *after* splitting, because it uses *training* set information to apply to the *validation* set, using a *Processor*.\n",
    "\n",
    "A *Processor* is a transformation that is applied to all the inputs once at initialization, with some *state* computed on the training set that is then applied without modification on the validation set (and maybe the test set or at inference time on a single item). For instance, it could be **processing texts** to **tokenize**, then **numericalize** them. In that case we want the validation set to be numericalized with exactly the same vocabulary as the training set.\n",
    "\n",
    "Another example is in **tabular data**, where we **fill missing values** with (for instance) the median computed on the training set. That statistic is stored in the inner state of the *Processor* and applied on the validation set.\n",
    "\n",
    "In our case, we want to **convert label strings to numbers** in a consistent and reproducible way. So we create a list of possible labels in the training set, and then convert our labels to numbers based on this *vocab*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=2368)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from collections import OrderedDict\n",
    "\n",
    "def uniqueify(x, sort=False):\n",
    "    res = list(OrderedDict.fromkeys(x).keys())\n",
    "    if sort: res.sort()\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's define the processor. We also define a `ProcessedItemList` with an `obj` method that can get the unprocessed items: for instance a processed label will be an index between 0 and the number of classes - 1, the corresponding `obj` will be the name of the class. The first one is needed by the model for the training, but the second one is better for displaying the objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Processor(): \n",
    "    def process(self, items): return items\n",
    "\n",
    "class CategoryProcessor(Processor):\n",
    "    def __init__(self): self.vocab=None\n",
    "    \n",
    "    def __call__(self, items):\n",
    "        #The vocab is defined on the first use.\n",
    "        if self.vocab is None:\n",
    "            self.vocab = uniqueify(items)\n",
    "            self.otoi  = {v:k for k,v in enumerate(self.vocab)}\n",
    "        return [self.proc1(o) for o in items]\n",
    "    def proc1(self, item):  return self.otoi[item]\n",
    "    \n",
    "    def deprocess(self, idxs):\n",
    "        assert self.vocab is not None\n",
    "        return [self.deproc1(idx) for idx in idxs]\n",
    "    def deproc1(self, idx): return self.vocab[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we label according to the folders of the images, so simply `fn.parent.name`. We label the training set first with a newly created `CategoryProcessor` so that it computes its inner `vocab` on that set. Then we label the validation set using the same processor, which means it uses the same `vocab`. The end result is another `SplitData` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def parent_labeler(fn): return fn.parent.name\n",
    "\n",
    "def _label_by_func(ds, f, cls=ItemList): return cls([f(o) for o in ds.items], path=ds.path)\n",
    "\n",
    "#This is a slightly different from what was seen during the lesson,\n",
    "#   we'll discuss the changes in lesson 11\n",
    "class LabeledData():\n",
    "    def process(self, il, proc): return il.new(compose(il.items, proc))\n",
    "\n",
    "    def __init__(self, x, y, proc_x=None, proc_y=None):\n",
    "        self.x,self.y = self.process(x, proc_x),self.process(y, proc_y)\n",
    "        self.proc_x,self.proc_y = proc_x,proc_y\n",
    "        \n",
    "    def __repr__(self): return f'{self.__class__.__name__}\\nx: {self.x}\\ny: {self.y}\\n'\n",
    "    def __getitem__(self,idx): return self.x[idx],self.y[idx]\n",
    "    def __len__(self): return len(self.x)\n",
    "    \n",
    "    def x_obj(self, idx): return self.obj(self.x, idx, self.proc_x)\n",
    "    def y_obj(self, idx): return self.obj(self.y, idx, self.proc_y)\n",
    "    \n",
    "    def obj(self, items, idx, procs):\n",
    "        isint = isinstance(idx, int) or (isinstance(idx,torch.LongTensor) and not idx.ndim)\n",
    "        item = items[idx]\n",
    "        for proc in reversed(listify(procs)):\n",
    "            item = proc.deproc1(item) if isint else proc.deprocess(item)\n",
    "        return item\n",
    "\n",
    "    @classmethod\n",
    "    def label_by_func(cls, il, f, proc_x=None, proc_y=None):\n",
    "        return cls(il, _label_by_func(il, f), proc_x=proc_x, proc_y=proc_y)\n",
    "\n",
    "def label_by_func(sd, f, proc_x=None, proc_y=None):\n",
    "    train = LabeledData.label_by_func(sd.train, f, proc_x=proc_x, proc_y=proc_y)\n",
    "    valid = LabeledData.label_by_func(sd.valid, f, proc_x=proc_x, proc_y=proc_y)\n",
    "    return SplitData(train,valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ll = label_by_func(sd, parent_labeler, proc_y=CategoryProcessor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert ll.train.proc_y is ll.valid.proc_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ItemList (12894 items)\n",
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll.train.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 'n03888257', ['n03888257', 'n03888257'])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll.train.y.items[0], ll.train.y_obj(0), ll.train.y_obj(slice(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SplitData\n",
       "Train: LabeledData\n",
       "x: ImageList (12894 items)\n",
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_9403.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_6402.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_4446.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_22655.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_29390.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_17004.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_8837.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_19451.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_12883.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/train/n03888257/n03888257_13476.JPEG')...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160\n",
       "y: ItemList (12894 items)\n",
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160\n",
       "\n",
       "Valid: LabeledData\n",
       "x: ImageList (500 items)\n",
       "[PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00016387.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00034544.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00009593.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00029149.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00037770.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00009370.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00031268.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00047147.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00031035.JPEG'), PosixPath('/home/ubuntu/.fastai/data/imagenette-160/val/n03888257/ILSVRC2012_val_00020698.JPEG')...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160\n",
       "y: ItemList (500 items)\n",
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0...]\n",
       "Path: /home/ubuntu/.fastai/data/imagenette-160\n"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform to tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=3044)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<PIL.Image.Image image mode=RGB size=160x243 at 0x7FB0CF0488D0>, 0)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll.train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x243 at 0x7FB0CF048E48>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll.train[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to put all our images in a batch, we need them to have all the same size. We can do this easily in PIL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FB0CF2FCE10>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ll.train[0][0].resize((128,128))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first transform resizes to a given size, then we convert the image to a by tensor before converting it to float and dividing by 255. We will investigate data augmentation transforms at length in notebook 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class ResizeFixed(Transform):\n",
    "    _order=10\n",
    "    def __init__(self,size):\n",
    "        if isinstance(size,int): size=(size,size)\n",
    "        self.size = size\n",
    "        \n",
    "    def __call__(self, item): return item.resize(self.size, PIL.Image.BILINEAR)\n",
    "\n",
    "def to_byte_tensor(item):\n",
    "    res = torch.ByteTensor(torch.ByteStorage.from_buffer(item.tobytes()))\n",
    "    w,h = item.size\n",
    "    return res.view(h,w,-1).permute(2,0,1)\n",
    "to_byte_tensor._order=20\n",
    "\n",
    "def to_float_tensor(item): return item.float().div_(255.)\n",
    "to_float_tensor._order=30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = [make_rgb, ResizeFixed(128), to_byte_tensor, to_float_tensor]\n",
    "\n",
    "il = ImageList.from_files(path, tfms=tfms)\n",
    "sd = SplitData.split_by_func(il, splitter)\n",
    "ll = label_by_func(sd, parent_labeler, proc_y=CategoryProcessor())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a little convenience function to show an image from the corresponding tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def show_image(im, figsize=(3,3)):\n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(im.permute(1,2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 128, 128])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = ll.train[0]\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataBunch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to put our datasets together in a `DataBunch`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=3226)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs=64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl,valid_dl = get_dls(ll.train,ll.valid,bs, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = next(iter(train_dl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 3, 128, 128])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can still see the images in a batch and get the corresponding classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n03888257'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(x[0])\n",
    "ll.train.proc_y.vocab[y[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 6, 2, 2, 6, 0, 0, 2, 3, 1, 0, 9, 8, 6, 2, 7, 4, 9, 4, 0, 1, 6, 8, 4,\n",
       "        7, 7, 0, 1, 0, 0, 3, 6, 1, 1, 8, 0, 3, 8, 1, 8, 8, 2, 1, 0, 3, 5, 3, 5,\n",
       "        5, 5, 5, 9, 7, 1, 4, 6, 6, 6, 1, 9, 2, 9, 4, 0])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We change a little bit our `DataBunch` to add a few attributes: `c_in` (for channel in) and `c_out` (for channel out) instead of just `c`. This will help when we need to build our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class DataBunch():\n",
    "    def __init__(self, train_dl, valid_dl, c_in=None, c_out=None):\n",
    "        self.train_dl,self.valid_dl,self.c_in,self.c_out = train_dl,valid_dl,c_in,c_out\n",
    "\n",
    "    @property\n",
    "    def train_ds(self): return self.train_dl.dataset\n",
    "\n",
    "    @property\n",
    "    def valid_ds(self): return self.valid_dl.dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define a function that goes directly from the `SplitData` to a `DataBunch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def databunchify(sd, bs, c_in=None, c_out=None, **kwargs):\n",
    "    dls = get_dls(sd.train, sd.valid, bs, **kwargs)\n",
    "    return DataBunch(*dls, c_in=c_in, c_out=c_out)\n",
    "\n",
    "SplitData.to_databunch = databunchify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us the full summary on how to grab our data and put it in a `DataBunch`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = datasets.untar_data(datasets.URLs.IMAGENETTE_160)\n",
    "tfms = [make_rgb, ResizeFixed(128), to_byte_tensor, to_float_tensor]\n",
    "\n",
    "il = ImageList.from_files(path, tfms=tfms)\n",
    "sd = SplitData.split_by_func(il, partial(grandparent_splitter, valid_name='val'))\n",
    "ll = label_by_func(sd, parent_labeler, proc_y=CategoryProcessor())\n",
    "data = ll.to_databunch(bs, c_in=3, c_out=10, num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=3360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbfs = [partial(AvgStatsCallback,accuracy),\n",
    "        CudaCallback]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will normalize with the statistics from a batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.4768, 0.4785, 0.4628], device='cuda:0'),\n",
       " tensor([0.2538, 0.2485, 0.2760], device='cuda:0'))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m,s = x.mean((0,2,3)).cuda(),x.std((0,2,3)).cuda()\n",
    "m,s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def normalize_chan(x, mean, std):\n",
    "    return (x-mean[...,None,None]) / std[...,None,None]\n",
    "\n",
    "_m = tensor([0.47, 0.48, 0.45])\n",
    "_s = tensor([0.29, 0.28, 0.30])\n",
    "norm_imagenette = partial(normalize_chan, mean=_m.cuda(), std=_s.cuda())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbfs.append(partial(BatchTransformXCallback, norm_imagenette))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nfs = [64,64,128,256]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build our model using [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/abs/1812.01187), in particular: we don't use a big conv 7x7 at first but three 3x3 convs, and don't go directly from 3 channels to 64 but progressively add those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import math\n",
    "def prev_pow_2(x): return 2**math.floor(math.log2(x))\n",
    "\n",
    "def get_cnn_layers(data, nfs, layer, **kwargs):\n",
    "    def f(ni, nf, stride=2): return layer(ni, nf, 3, stride=stride, **kwargs)\n",
    "    l1 = data.c_in\n",
    "    l2 = prev_pow_2(l1*3*3)\n",
    "    layers =  [f(l1  , l2  , stride=1),\n",
    "               f(l2  , l2*2, stride=2),\n",
    "               f(l2*2, l2*4, stride=2)]\n",
    "    nfs = [l2*4] + nfs\n",
    "    layers += [f(nfs[i], nfs[i+1]) for i in range(len(nfs)-1)]\n",
    "    layers += [nn.AdaptiveAvgPool2d(1), Lambda(flatten), \n",
    "               nn.Linear(nfs[-1], data.c_out)]\n",
    "    return layers\n",
    "\n",
    "def get_cnn_model(data, nfs, layer, **kwargs):\n",
    "    return nn.Sequential(*get_cnn_layers(data, nfs, layer, **kwargs))\n",
    "\n",
    "def get_learn_run(nfs, data, lr, layer, cbs=None, opt_func=None, **kwargs):\n",
    "    model = get_cnn_model(data, nfs, layer, **kwargs)\n",
    "    init_cnn(model)\n",
    "    return get_runner(model, data, lr=lr, cbs=cbs, opt_func=opt_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sched = combine_scheds([0.3,0.7], cos_1cycle_anneal(0.1,0.3,0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.2, conv_layer, cbs=cbfs+[\n",
    "    partial(ParamScheduler, 'lr', sched)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at our model using Hooks. We print the layers and the shapes of their outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def model_summary(run, learn, data, find_all=False):\n",
    "    xb,yb = get_batch(data.valid_dl, run)\n",
    "    device = next(learn.model.parameters()).device#Model may not be on the GPU yet\n",
    "    xb,yb = xb.to(device),yb.to(device)\n",
    "    mods = find_modules(learn.model, is_lin_layer) if find_all else learn.model.children()\n",
    "    f = lambda hook,mod,inp,out: print(f\"{mod}\\n{out.shape}\\n\")\n",
    "    with Hooks(mods, f) as hooks: learn.model(xb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "  (1): GeneralRelu()\n",
      "  (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "torch.Size([128, 16, 128, 128])\n",
      "\n",
      "Sequential(\n",
      "  (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (1): GeneralRelu()\n",
      "  (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "torch.Size([128, 32, 64, 64])\n",
      "\n",
      "Sequential(\n",
      "  (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (1): GeneralRelu()\n",
      "  (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "torch.Size([128, 64, 32, 32])\n",
      "\n",
      "Sequential(\n",
      "  (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (1): GeneralRelu()\n",
      "  (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "torch.Size([128, 64, 16, 16])\n",
      "\n",
      "Sequential(\n",
      "  (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (1): GeneralRelu()\n",
      "  (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "torch.Size([128, 64, 8, 8])\n",
      "\n",
      "Sequential(\n",
      "  (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (1): GeneralRelu()\n",
      "  (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "torch.Size([128, 128, 4, 4])\n",
      "\n",
      "Sequential(\n",
      "  (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  (1): GeneralRelu()\n",
      "  (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n",
      "torch.Size([128, 256, 2, 2])\n",
      "\n",
      "AdaptiveAvgPool2d(output_size=1)\n",
      "torch.Size([128, 256, 1, 1])\n",
      "\n",
      "Lambda()\n",
      "torch.Size([128, 256])\n",
      "\n",
      "Linear(in_features=256, out_features=10, bias=True)\n",
      "torch.Size([128, 10])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_summary(run, learn, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can train the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [1.7975745138242594, tensor(0.3771, device='cuda:0')]\n",
      "valid: [1.950084228515625, tensor(0.3640, device='cuda:0')]\n",
      "train: [1.331341733558244, tensor(0.5549, device='cuda:0')]\n",
      "valid: [1.182614013671875, tensor(0.6160, device='cuda:0')]\n",
      "train: [1.0004353405653792, tensor(0.6729, device='cuda:0')]\n",
      "valid: [0.9452028198242187, tensor(0.6740, device='cuda:0')]\n",
      "train: [0.744675257750698, tensor(0.7583, device='cuda:0')]\n",
      "valid: [0.8292762451171874, tensor(0.7360, device='cuda:0')]\n",
      "train: [0.5341721137253761, tensor(0.8359, device='cuda:0')]\n",
      "valid: [0.798895751953125, tensor(0.7360, device='cuda:0')]\n",
      "CPU times: user 25.6 s, sys: 10.7 s, total: 36.4 s\n",
      "Wall time: 1min 7s\n"
     ]
    }
   ],
   "source": [
    "%time run.fit(5, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [leaderboard](https://github.com/fastai/imagenette/blob/master/README.md) as this notebook is written has ~85% accuracy for 5 epochs at 128px size, so we're definitely on the right track!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 08_data_block.ipynb to exp/nb_08.py\r\n"
     ]
    }
   ],
   "source": [
    "!python notebook2script.py 08_data_block.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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 "nbformat": 4,
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